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Machine learning in process systems engineering: Challenges and opportunities

Authors: Prodromos Daoutidis,Jay H. Lee,Srinivas Rangarajan,Leo Chiang,Bhushan Gopaluni,Artur M. Schweidtmann,Iiro Harjunkoski,Mehmet Mercangöz,Ali Mesbah,Fani Boukouvala,Fernando V. Lima,Antonio del Rio Chanona,Christos Georgakis
Journal: Computers
Publisher: Elsevier BV
Publish date: 2024-2
ISSN: 0098-1354 DOI: 10.1016/j.compchemeng.2023.108523
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The entire paper rests on discussions at a session featuring top PSE-ML researchers. This creates a massive selection bias.

No Skeptics? Where were the experts who question if ML is the real bottleneck, and not, say, our ability to solve complex non-convex problems with traditional methods?
Echo Chamber? With a pre-selected, pro-ML group, the challenges identified simply validate their own research agendas.
Black Box Discussions: The paper gives zero methodology for how consensus was reached. Were dissenting views even presented or recorded?

Without a transparent, critical process, this isn’t a field-wide roadmap; it’s a summary of an insider meeting, potentially misdirecting the community’s priorities.

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